Extremely Stable Ag-Based Photonics, Plasmonic, Optical, and Electronic Materials and Devices Designed with Surface Chemistry Engineering for Anti-Tarnish.

Silver (Ag) metal-based structures are promising building blocks for next-generation photonics and electronics owing to their unique characteristics, such as high reflectivity, surface plasmonic resonance effects, high electrical conductivity, and tunable electron transport mechanisms. However, Ag structures exhibit poor sustainability in terms of device performance because harsh chemicals, particularly S2- ions present in the air, can damage their structures, lowering their optical and electrical properties. Here, the surface chemistry of Ag structures with (3-mercaptopropyl)trimethoxysilane (MPTS) ligands at room temperature and under ambient conditions is engineered to prevent deterioration of their optical and electrical properties owing to S2- exposure. Regardless of the dimensions of the Ag structures, the MPTS ligands can be applied to each dimension (0D, 1D, and 3D). Consequently, highly sustainable plasmonic effects (Δλ < 2 nm), Fabry-Perot cavity resonance structures (Δλ < 2 nm), reflectors (ΔRReflectance < 0.5%), flexible electrodes (ΔRelectrical < 0.1 Ω), and strain gauge sensors (ΔGF < 1), even in S2- exposing conditions is achieved. This strategy is believed to significantly contribute to environmental pollution reduction by decreasing the volume of electronic waste.

High-Performance Self-Powered Quantum Dot Infrared Photodetector with Azide Ion Solution Treated Electron Transport Layer.

The demand for self-powered photodetectors (PDs) capable of NIR detection without external power is growing with the advancement of NIR technologies such as LIDAR and object recognition. Lead sulfide quantum dot-based photodetectors (PbS QPDs) excel in NIR detection; however, their self-powered operation is hindered by carrier traps induced by surface defects and unfavorable band alignment in the zinc oxide nanoparticle (ZnO NP) electron-transport layer (ETL). In this study, an effective azide-ion (N3 - ) treatment is introduced on a ZnO NP ETL to reduce the number of traps and improve the band alignment in a PbS QPD. The ZnO NP ETL treated with azide ions exhibited notable improvements in carrier lifetime and mobility as well as an enhanced internal electric field within the thin-film heterojunction of the ZnO NPs and PbS QDs. The azide-ion-treated PbS QPD demonstrated a increase in short-circuit current density upon NIR illumination, marking a responsivity of 0.45 A W-1 , specific detectivity of 4 × 1011 Jones at 950 nm, response time of 8.2 µs, and linear dynamic range of 112 dB.

Generation of Conventional 18F-FDG PET Images from 18F-Florbetaben PET Images Using Generative Adversarial Network: A Preliminary Study Using ADNI Dataset.

Background and Objectives: 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) (PETFDG) image can visualize neuronal injury of the brain in Alzheimer's disease. Early-phase amyloid PET image is reported to be similar to PETFDG image. This study aimed to generate PETFDG images from 18F-florbetaben PET (PETFBB) images using a generative adversarial network (GAN) and compare the generated PETFDG (PETGE-FDG) with real PETFDG (PETRE-FDG) images using the structural similarity index measure (SSIM) and the peak signal-to-noise ratio (PSNR). Materials and Methods: Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, 110 participants with both PETFDG and PETFBB images at baseline were included. The paired PETFDG and PETFBB images included six and four subset images, respectively. Each subset image had a 5 min acquisition time. These subsets were randomly sampled and divided into 249 paired PETFDG and PETFBB subset images for the training datasets and 95 paired subset images for the validation datasets during the deep-learning process. The deep learning model used in this study is composed of a GAN with a U-Net. The differences in the SSIM and PSNR values between the PETGE-FDG and PETRE-FDG images in the cycleGAN and pix2pix models were evaluated using the independent Student's t-test. Statistical significance was set at p ≤ 0.05. Results: The participant demographics (age, sex, or diagnosis) showed no statistically significant differences between the training (82 participants) and validation (28 participants) groups. The mean SSIM between the PETGE-FDG and PETRE-FDG images was 0.768 ± 0.135 for the cycleGAN model and 0.745 ± 0.143 for the pix2pix model. The mean PSNR was 32.4 ± 9.5 and 30.7 ± 8.0. The PETGE-FDG images of the cycleGAN model showed statistically higher mean SSIM than those of the pix2pix model (p < 0.001). The mean PSNR was also higher in the PETGE-FDG images of the cycleGAN model than those of pix2pix model (p < 0.001). Conclusions: We generated PETFDG images from PETFBB images using deep learning. The cycleGAN model generated PETGE-FDG images with a higher SSIM and PSNR values than the pix2pix model. Image-to-image translation using deep learning may be useful for generating PETFDG images. These may provide additional information for the management of Alzheimer's disease without extra image acquisition and the consequent increase in radiation exposure, inconvenience, or expenses.

The Use of F-18 FDG PET-Based Cognitive Reserve to Evaluate Cognitive Decline in Alzheimer's Disease, Independent of Educational Influence.

Background and Objectives: The optimal assessment of cognitive function, including the impact of education, is crucial in managing Alzheimer's disease (AD). This study aimed to evaluate the role of cognitive reserve (CR), represented by the metabolic status of regions of the cerebral cortex, to evaluate cognitive decline considering the educational attainment of patients with AD. Materials and Methods: We used data from the Alzheimer's Disease Neuroimaging Initiative database, and selected 124 patients who underwent both baseline F-18 fluorodeoxyglucose (FDG) and F-18 florbetaben (FBB) positron emission tomography (PET) scans. Demographics, cognitive function variables (Clinical Dementia Rating-Sum of Boxes [CDR]; AD Assessment Scale 11/13 [ADAS11/13] Mini-Mental State Examination [MMSE]), and the average standardized uptake value ratio (SUVR) of cerebral cortex regions to those of the cerebellum were obtained from the data. The participants' education level was divided into low and high education subgroups using four cut-offs of 12, 14, 16, and 18 years of educational attainment (G12, G14, G16, and G18, respectively). Demographic and cognitive function variables were compared between the two subgroups in each of the four groups, and their correlations with the SUVRs were evaluated. Results: There was no significant difference between the high and low education subgroups in each of the four groups, except for ADAS11/13 and MMSE in G14 and age in G16. The SUVRs of FDG PET (FDGSUVR) were significantly correlated with CDR, ADAS11/13, and MMSE scores. FDGSUVR showed different trajectories of neurodegeneration between the low and high education groups. Conclusions: FDGSUVR correlated moderately but significantly with neuropsychological test results, without being influenced by education level. Therefore, FDG PET may reflect CR independent of education level, and therefore could be a reliable tool to evaluate cognitive decline in AD.

Assessment of Instability in Thoracolumbar Burst Fractures Using a New Bone Scan Scoring System.

Background and Objectives: Unstable thoracolumbar burst fractures require surgical management as they can result in neurological deficits if left untreated. This study aimed to evaluate whether a new bone scan scoring system could accurately assess instability in thoracolumbar burst fractures. Materials and Methods: Fifty-two patients with thoracolumbar burst fractures who underwent bone scans and magnetic resonance imaging prior to surgery between January 2015 and August 2017 at Ulsan University Hospital were selected for inclusion. Instability was determined by clinical assessment and imaging, and the Thoracolumbar Injury Classification and Severity score was determined. Bone scans were visually evaluated using a new bone scan scoring system. Bone scan findings of vertebral body (BB) and posterior column (BP) were scored separately and were summed to produce BTS {BTS (total score) = BB (body score, 5 points) + BP (posterior score, 2 points)}. The diagnostic performance of the scoring system for identifying unstable then thoracolumbar burst fractures were assessed. Results: Of the 52 thoracolumbar burst fractures, 34 (65.4%) were unstable and 31 (59.6%) had a Thoracolumbar Injury Classification and Severity score ≥ 5. The diagnostic performance of using BTS ≥ 4 to identify unstable thoracolumbar burst fractures and those with a Thoracolumbar Injury Classification and Severity score ≥ 5 was as follows: sensitivity, 61.8% and 58.1%; specificity, 94.4% and 81.0%; positive predictive value, 95.5% and 81.8%; and negative predictive value, 56.7% and 56.7%, respectively. Conclusions: The proposed bone scan scoring system has a high specificity and positive predictive value for identifying thoracolumbar burst fractures that are unstable or have a Thoracolumbar Injury Classification and Severity score ≥ 5. This scoring system may help to inform decisions regarding surgical management.

χ-separation: Magnetic susceptibility source separation toward iron and myelin mapping in the brain.

Obtaining a histological fingerprint from the in-vivo brain has been a long-standing target of magnetic resonance imaging (MRI). In particular, non-invasive imaging of iron and myelin, which are involved in normal brain functions and are histopathological hallmarks in neurodegenerative diseases, has practical utilities in neuroscience and medicine. Here, we propose a biophysical model that describes the individual contribution of paramagnetic (e.g., iron) and diamagnetic (e.g., myelin) susceptibility sources to the frequency shift and transverse relaxation of MRI signals. Using this model, we develop a method, χ-separation, that generates the voxel-wise distributions of the two sources. The method is validated using computer simulation and phantom experiments, and applied to ex-vivo and in-vivo brains. The results delineate the well-known histological features of iron and myelin in the specimen, healthy volunteers, and multiple sclerosis patients. This new technology may serve as a practical tool for exploring the microstructural information of the brain.

Machine Learning Analysis to Identify Digital Behavioral Phenotypes for Engagement and Health Outcome Efficacy of an mHealth Intervention for Obesity: Randomized Controlled Trial.

BACKGROUND: The digital health care community has been urged to enhance engagement and clinical outcomes by analyzing multidimensional digital phenotypes. OBJECTIVE: This study aims to use a machine learning approach to investigate the performance of multivariate phenotypes in predicting the engagement rate and health outcomes of digital cognitive behavioral therapy. METHODS: We leveraged both conventional phenotypes assessed by validated psychological questionnaires and multidimensional digital phenotypes within time-series data from a mobile app of 45 participants undergoing digital cognitive behavioral therapy for 8 weeks. We conducted a machine learning analysis to discriminate the important characteristics. RESULTS: A higher engagement rate was associated with higher weight loss at 8 weeks (r=-0.59; P<.001) and 24 weeks (r=-0.52; P=.001). Applying the machine learning approach, lower self-esteem on the conventional phenotype and higher in-app motivational measures on digital phenotypes commonly accounted for both engagement and health outcomes. In addition, 16 types of digital phenotypes (ie, lower intake of high-calorie food and evening snacks and higher interaction frequency with mentors) predicted engagement rates (mean R2 0.416, SD 0.006). The prediction of short-term weight change (mean R2 0.382, SD 0.015) was associated with 13 different digital phenotypes (ie, lower intake of high-calorie food and carbohydrate and higher intake of low-calorie food). Finally, 8 measures of digital phenotypes (ie, lower intake of carbohydrate and evening snacks and higher motivation) were associated with a long-term weight change (mean R2 0.590, SD 0.011). CONCLUSIONS: Our findings successfully demonstrated how multiple psychological constructs, such as emotional, cognitive, behavioral, and motivational phenotypes, elucidate the mechanisms and clinical efficacy of a digital intervention using the machine learning method. Accordingly, our study designed an interpretable digital phenotype model, including multiple aspects of motivation before and during the intervention, predicting both engagement and clinical efficacy. This line of research may shed light on the development of advanced prevention and personalized digital therapeutics. TRIAL REGISTRATION: ClinicalTrials.gov NCT03465306; https://clinicaltrials.gov/ct2/show/NCT03465306.

Clinical efficacy and plausibility of a smartphone-based integrated online real-time diabetes care system via glucose and diet data management: a pilot study.

BACKGROUND: Smartphones have become novel healthcare tools for patients with diabetes. However, it is uncertain whether the smartphone application support system helps in glycaemic control in patients with type 2 diabetes. AIMS: To evaluate the efficacy and plausibility of smartphone-based integrated online real-time diabetes care. METHODS: Forty patients with type 2 diabetes were randomly assigned to the smartphone-based care (SC) (n = 20) and conventional care (CC) (n = 20) groups for 12 weeks. The SC group was instructed to use smartphone application (Noom Coach) and a glucose meter, and was provided medical supervision based on blood glucose level and food intake information sent to the central database server. The efficacy was evaluated by glycated haemoglobin (A1C ≤ 6.5%). The Summary of Diabetes Self-Care Activities (SDSCA) questionnaire was collected at baseline and at week 12. RESULTS: Seventeen and 18 patients of the SC and CC groups completed the study respectively. In the SC group, more patients achieved target A1C compared with the CC group (47.1% vs 11.1%, P = 0.019). In both group, SDSCA scores excluding the exercise item showed overall improvement (general diet, 1.4 ± 2.0 → 2.6 ± 2.3 vs 0.4 ± 1.1 → 1.8 ± 2.2; specific diet, 4.2 ± 1.7 → 5.4 ± 1.2 vs 3.8 ± 1.6 → 5.1 ± 1.1; blood glucose test, 3.3 ± 2.8 → 4.9 ± 2.3 vs 1.0 ± 2.2 → 4.7 ± 2.3; foot care, 1.5 ± 1.6 → 3.6 ± 2.8 vs 1.4 ± 1.9 → 6.1 ± 1.4; all P < 0.05). There was no difference between both groups other than the aspect of foot care (P = 0.008). CONCLUSIONS: The smartphone-based integrated online real-time diabetes care system through glucose and diet data management showed clinical plausibility in glucose control in real clinical practice.

Exploring Abnormal Behavior Patterns of Online Users With Emotional Eating Behavior: Topic Modeling Study.

BACKGROUND: Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS). OBJECTIVE: This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system. METHODS: The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent-based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA. RESULTS: The following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P<.001). CONCLUSIONS: This study introduces a data-driven approach for analyzing behavior patterns of social website users with EE behaviors. We discovered the possibility of the LDA topic model as an exploratory user study method for abnormal behaviors in medical research. We also investigated the possibilities of ML- and topic modeling-based classifiers to automatically categorize text-based behavioral data, which could be applied to personalized medicine in future research.

Amodiaquine improves insulin resistance and lipid metabolism in diabetic model mice.

AIMS: Although peroxisome proliferator-activated receptors (PPARs)α/γ dual agonists can be beneficial for treatment of dyslipidemia in patients with type 2 diabetes, their use is limited owing to various side effects, including body weight gain, edema, and heart failure. We aimed to demonstrate that amodiaquine, an antimalarial agent, has potential as a PPARα/γ dual agonist with low risk of adverse effects. METHODS: We screened a Prestwick library (Prestwick Chemical; Illkirch, France) to identify novel PPARα/γ dual agonists and selected amodiaquine (4-[(7-chloroquinolin-4-yl)amino]-2-[(diethylamino)methyl]phenol), which activated both PPAR-α & -γ, for further investigation. We performed both in vitro, including glucose uptake assay and fatty acid oxidation assay, and in vivo studies to elucidate the anti-diabetic and anti-obesity effects of amodiaquine. RESULTS: Amodiaquine selectively activated the transcriptional activities of PPARα/γ and enhanced both fatty acid oxidation and glucose uptake without altering insulin secretion in vitro. In high-fat diet-induced obese and genetically modified obese/diabetic mice, amodiaquine not only remarkably ameliorated insulin resistance, hyperlipidemia, and fatty liver but also decreased body weight gain. CONCLUSION: Our findings suggest that amodiaquine exerts beneficial effects on glucose and lipid metabolism by concurrent activation of PPARα/γ. Furthermore, amodiaquine acts as an alternative insulin-sensitizing agent with a positive influence on lipid metabolism and has potential to prevent and treat type 2 diabetes while reducing the risk of lipid abnormalities.

Central administration of GLP-1 and GIP decreases feeding in mice.

Glucagon-like peptide-1 amide (GLP-1) and gastric inhibitory polypeptide (GIP) are incretin hormones regulating energy metabolism. GLP-1 and GIP combination is suggested as a promising therapeutic strategy for treatment of obesity and diabetes. However, the neuronal mechanisms are not yet investigated. In the present study, we investigated the role of central GLP-1 and GIP in regulation of body weight homeostasis. The effect of GLP-1 with GIP on food intake, body weight, locomotor activity were determined following intracerebroventricular (ICV) administration of GLP-1 and/or GIP in mice. ICV administration of low dose GLP-1 (0.3 nmol) and GIP (1 and 3 nmol) did not change food intake. However, ICV administration of higher doses GLP-1 (1 and 3 nmol) and GIP (6 nmol) significantly decreased food intake and body weight. To investigate the synergic effect of ICV GLP-1 and GIP, subeffective dose GLP-1 (0.3 nmol) and subeffective dose GIP (1 nmol) were chosen for further co-administration study. ICV co-administration of GLP-1 and GIP significantly decreased food intake, body weight and drinking. ICV co-administration of GLP-1 and GIP significantly increased neuronal activation and pro-opiomelanocortin (POMC) expression in hypothalamic arcuate nucleus. The neuronal activation and POMC expression were observed in two distinct neuronal populations. These results provide neuronal mechanisms supporting the development of GLP-1 and GIP combination therapeutics for treatment of obesity and diabetes.

Genetic analysis of parathyroid and pancreatic tumors in a patient with multiple endocrine neoplasia type 1 using whole-exome sequencing.

BACKGROUND: Multiple endocrine neoplasia type 1 (MEN1) syndrome is an autosomal dominant hereditary disorder characterized by the presence of endocrine tumors affecting the parathyroid, pancreas, and pituitary. A heterozygous germline inactivating mutation in the MEN1 gene (first hit) may be followed by somatic loss of the remaining normal copy or somatic mutations in the MEN1 gene (second hit). Whole-exome sequencing has been successfully used to elucidate the mutations associated with the different types of tumors. CASE PRESENTATION: We performed whole-exome sequencing (WES) on three parathyroid tumors, one pancreatic insulinoma, and a blood sample taken from the same patient with MEN1 to study tumor heterogeneity in MEN1 originating from different tumors. We identified a novel frame-shift deletion (c.1382_1383delAG, p.E461GfsX69) in the MEN1 gene using WES, which was confirmed by Sanger sequencing. WES and the SNP array revealed somatic LOH on chromosome 11 in parathyroid tumors (left upper, left lower, and right upper parathyroid). However, we did not detect a somatic MEN1 gene mutation or LOH in the pancreatic insulinoma. WES revealed two somatic functional variants outside the MEN1 gene in the pancreatic insulinoma. CONCLUSIONS: This study revealed heterogeneity among tumors in the same patient with MEN1, suggesting that different tumor-specific tumorigenic mechanisms may contribute to the pathogenesis of MEN1 tumors. The present study supports the clinical applicability of the WES strategy to research on multiple tumor samples and blood.

Multistage genome-wide association meta-analyses identified two new loci for bone mineral density.

Aiming to identify novel genetic variants and to confirm previously identified genetic variants associated with bone mineral density (BMD), we conducted a three-stage genome-wide association (GWA) meta-analysis in 27 061 study subjects. Stage 1 meta-analyzed seven GWA samples and 11 140 subjects for BMDs at the lumbar spine, hip and femoral neck, followed by a Stage 2 in silico replication of 33 SNPs in 9258 subjects, and by a Stage 3 de novo validation of three SNPs in 6663 subjects. Combining evidence from all the stages, we have identified two novel loci that have not been reported previously at the genome-wide significance (GWS; 5.0 × 10(-8)) level: 14q24.2 (rs227425, P-value 3.98 × 10(-13), SMOC1) in the combined sample of males and females and 21q22.13 (rs170183, P-value 4.15 × 10(-9), CLDN14) in the female-specific sample. The two newly identified SNPs were also significant in the GEnetic Factors for OSteoporosis consortium (GEFOS, n = 32 960) summary results. We have also independently confirmed 13 previously reported loci at the GWS level: 1p36.12 (ZBTB40), 1p31.3 (GPR177), 4p16.3 (FGFRL1), 4q22.1 (MEPE), 5q14.3 (MEF2C), 6q25.1 (C6orf97, ESR1), 7q21.3 (FLJ42280, SHFM1), 7q31.31 (FAM3C, WNT16), 8q24.12 (TNFRSF11B), 11p15.3 (SOX6), 11q13.4 (LRP5), 13q14.11 (AKAP11) and 16q24 (FOXL1). Gene expression analysis in osteogenic cells implied potential functional association of the two candidate genes (SMOC1 and CLDN14) in bone metabolism. Our findings independently confirm previously identified biological pathways underlying bone metabolism and contribute to the discovery of novel pathways, thus providing valuable insights into the intervention and treatment of osteoporosis.

Incidence of permanent hypocalcaemia after total thyroidectomy with or without central neck dissection for thyroid carcinoma: a nationwide claim study.

OBJECTIVE: Permanent hypocalcaemia is the most common and serious complication after total thyroidectomy (TT). This study examined the impact of central neck dissection (CND) and institutional volume on rates of permanent hypocalcaemia by analysing data held in the nationwide claim database of South Korea. DESIGN: Data from patients who underwent TT due to thyroid carcinoma from 2007-2013 were obtained from the Health Insurance Review and Assessment Service database. Of these, patients prescribed more than 1000 mg of elemental calcium for more than 288 days during the first 360 days postsurgery were defined as having permanent hypocalcaemia. RESULTS: In total, 192 333 patients (32 988 male and 159 345 female) were eligible for analysis. Of these, 52 707 (27·4%) underwent TT alone and 139 626 (72·6%) underwent TT plus CND. The incidence of permanent hypocalcaemia was greater in the TT plus CND group than in the TT alone group (5·4% vs 4·6%, P < 0·001). The age- and sex-adjusted risk for permanent hypocalcaemia in the TT plus CND group was 1·20 (P < 0·001). CND did not raise the rates of permanent hypocalcaemia in institutes with a low volume of annual cases (<200), whereas permanent hypocalcaemia was more common in the TT plus CND group than in the TT alone group (3·5% vs 2·9%, P = 0·002) in institutes with a large volume of annual cases (≥800). CONCLUSIONS: TT plus CND was associated with a greater risk of permanent hypocalcaemia than TT alone. Surgeons should consider the risk of permanent hypocalcaemia when deciding whether to perform CND.

Utilizing Genetic Predisposition Score in Predicting Risk of Type 2 Diabetes Mellitus Incidence: A Community-based Cohort Study on Middle-aged Koreans.

Contribution of genetic predisposition to risk prediction of type 2 diabetes mellitus (T2DM) was investigated using a prospective study in middle-aged adults in Korea. From a community cohort of 6,257 subjects with 8 yr' follow-up, genetic predisposition score with subsets of 3, 18, 36 selected single nucleotide polymorphisms (SNPs) (genetic predisposition score; GPS-3, GPS-18, GPS-36) in association with T2DM were determined, and their effect was evaluated using risk prediction models. Rs5215, rs10811661, and rs2237892 were in significant association with T2DM, and hazard ratios per risk allele score increase were 1.11 (95% confidence intervals: 1.06-1.17), 1.09 (1.01-1.05), 1.04 (1.02-1.07) with GPS-3, GPS-18, GPS-36, respectively. Changes in AUC upon addition of GPS were significant in simple and clinical models, but the significance disappeared in full clinical models with glycated hemoglobin (HbA1c). For net reclassification index (NRI), significant improvement observed in simple (range 5.1%-8.6%) and clinical (3.1%-4.4%) models were no longer significant in the full models. Influence of genetic predisposition in prediction ability of T2DM incidence was no longer significant when HbA1c was added in the models, confirming HbA1c as a strong predictor for T2DM risk. Also, the significant SNPs verified in our subjects warrant further research, e.g. gene-environmental interaction and epigenetic studies.

Prevalence of vitamin D deficiency and effects of supplementation with cholecalciferol in patients with chronic kidney disease.

OBJECTIVE: We aimed to evaluate the vitamin D status, the effect of cholecalciferol supplementation, and the factors associated with vitamin D restoration in nondialytic patients with chronic kidney disease (CKD). DESIGN: The present study was a prospective open-label trial. SETTING: This study took place at the Seoul National University Boramae Medical Center. SUBJECTS: Patients with nondialytic CKD (estimated glomerular filtration rate [eGFR] 10-59 mL/min per 1.73 m(2)) participated in this study. INTERVENTION: Vitamin D status in 210 CKD patients was assessed and the patients with vitamin D deficiency (<30 ng/mL) were administered cholecalciferol (1,000 IU/day) for 6 months. MAIN OUTCOME MEASURE: The restoration rate of vitamin D deficiency at 3 and 6 months and the response-related factors were analyzed. RESULTS: The prevalence of vitamin D deficiency was 40.7% in CKD Stage 3, 61.5% in Stage 4, and 85.7% in Stage 5. The subgroup with vitamin D deficiency had a greater proportion of patients with diabetes, lower eGFR, and higher proteinuria. With the supplementation, 52 patients (76.5%) reached levels of 25-hydroxy vitamin D (25(OH)D) of 30 ng/mL or greater at 3 months, and the restoration of vitamin D was observed in 61 patients (89.7%) at 6 months. Lower levels of 25(OH)D and a higher amount of proteinuria at baseline were the factors associated with lower response to vitamin D supplementation. CONCLUSION: Vitamin D deficiency rate was high in nondialytic CKD patients, and the proportion increased as renal function decreased. A higher amount of proteinuria was the independent risk factor of nonresponse with supplementation. Vitamin D was replenished in most patients with cholecalciferol supplementation without any significant adverse effects.

Case Reports of Binge Eating Patterns in the Recovery Phase of Anorexia Nervosa Patients With and Without Food Addiction.

Food addiction refers to a condition in which individuals exhibit addictive-like behaviors toward food, like those observed in substance abuse. Although still debated, evidence supporting the validity and usefulness of the concept of food addiction is growing. Food addiction is particularly associated with obesity and eating disorders involving binge eating. This study discusses the cases of two adolescent patients who presented with anorexia nervosa. During the recovery phase of anorexia nervosa, binge eating was observed, and the patterns of binge eating significantly differed between patients, with and without food addiction. Therefore, healthcare professionals treating eating disorders should be aware of food addiction and modify their treatment strategies accordingly.

Erratum: Assessment Methods for Problematic Eating Behaviors in Children and Adolescents With Autism Spectrum Disorder.

[This corrects the article on p. 57 in vol. 35, PMID: 38204745.].

Assessment Methods for Problematic Eating Behaviors in Children and Adolescents With Autism Spectrum Disorder.

Autism spectrum disorder (ASD) can be associated with eating problems. However, currently, there is a lack of established guidelines for assessing and addressing eating behaviors in individuals with ASD. This gap in research exists due to the challenges associated with using traditional assessment methods, which may lead to discrepancies in responses and unintentional potential biases from caregivers. In this review, we provided a comprehensive overview of various eating behaviors commonly observed in individuals with ASD. These behaviors include 1) food neophobia, 2) selective eating, 3) binge eating, 4) food avoidance, 5) chewing and swallowing problems, 6) pica, 7) rumination, 8) rituals, and 9) problematic behaviors. Furthermore, we provide a perspective of utilizing digital tools: 1) augmentative and alternative communication; 2) ecological momentary assessment; and 3) video analysis, behavioral analysis, and facial expression analysis. This review explores existing assessment methods and suggests novel assessment aiding together.